Published April 28, 2026 | Version 1.0.0
Software Open

Reproducibility Package for "Weather-based prediction of black locust (Robinia pseudoacacia L.) honey yield and quality: A reproducible computational system with operational deployment"

Authors/Creators

  • 1. Apiculture Division, National Institute of Agricultural Sciences, Rural Development Administration, Wanju, Republic of Korea

Description

This package provides full computational reproducibility for the manuscript:
 
"Weather-based prediction of black locust (Robinia pseudoacacia L.) honey yield 
and quality: A reproducible computational system with operational deployment" 
(submitted to Computers and Electronics in Agriculture).
 
The package contains the canonical analysis script, automatic verification 
script, retrospective decomposition analysis, and figure generation code, 
along with the anonymized 570-record farm-year dataset (95 black locust 
apiaries across 18 KMA ASOS reference cities, 2020–2025) required to 
reproduce all numerical values reported in the manuscript.
 
KEY COMPONENTS:
 
- paper1_CANONICAL.py — Single canonical script implementing all score functions, 
  weights, OLS regression, and 4-fold cross-validation (LOYO, Spatial, LOCO, 
  Cross-type). Reproduces every numerical value in the manuscript.
 
- verify_paper1.py — Automatic verification script that checks 22 reported 
  manuscript values against tolerance-controlled expected values. Expected 
  output: 22/22 checkpoints PASSED.
 
- decompose_v6.py + robustness_analysis.py — Section 3.7 retrospective 
  decomposition: variance partitioning, all-pairs ranking, and permutation 
  testing for the 2020 vs 2024 production gap.
 
- figure11_generator.py — Generates Figure 11 (4-panel decomposition plot) 
  at publication quality.
 
- Two anonymized CSV datasets (fixed and migratory apiaries) with 
  pre-computed 3-stage weather aggregations.
 
- breakpoints_original.json — Segmented regression outputs supporting 
  Table 1 (BIC comparisons and bootstrap 95% CI).
 
PRIVACY: Original farm-level addresses have been replaced with sequential 
site identifiers (FX_NNN for fixed, MG_NNN for migratory apiaries). Only 
KMA ASOS reference station names and regional categories (northern/central/
southern) are retained. The mapping between site_id and original farm 
addresses is held privately by the corresponding author and not released.
 
USAGE: See README.md for the complete reproducibility workflow. The full 
pipeline executes in approximately 10–15 minutes on standard desktop hardware.
 
LICENSING: Code is released under the MIT License; data is released under 
Creative Commons Attribution 4.0 International (CC BY 4.0).
 
FUNDING: This work was supported by the Cooperative Research Program for 
Agriculture Science and Technology Development (Project No. RS-2024-00339097), 
Rural Development Administration, Republic of Korea.
 
AUTHORSHIP: This work was conducted under the supervision of Dr. Soon Ok Woo (Head of Apiculture Division, corresponding author and project leader, Project No. RS-2024-00339097). The first author (Sung-Hyun Min) led the data analysis, computational modeling, and manuscript preparation using the laboratory's accumulated multi-year apiary survey records. Co-authors contributed to data collection, curation, and validation.

Files

breakpoints_original.json

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Additional details

Funding

Rural Development Administration
Cooperative Research Program for Agriculture Science and Technology Development RS-2024-00339097

Dates

Created
2026-04-28
Canonical analysis date (paper1_CANONICAL.py validation: 22/22 PASS

Software

Programming language
Python